Prior work uses observation but does not consider interventions and focus on likelihood learning or hypothesis equivalence classes [1]}.
Later, approaches have also been proposed to infer the causal direction only from observation [2]}, based on not generally robust assumptions on the underlying causal graph.
The impact of interventions on probabilistic graphical models is also introduced in [3]}.
Another early work [4]} uses intervention to detect causal direction, but it does not address representation learning, which is an important problem in the modern neural network and the main topic of this paper. Further, our method should be more efficient than the fast approximation method [4]}, which takes iterations to fit a model.
In contrast, we only run one forward pass on a fixed model.
